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  <title>Description of visualize_data</title>
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<h1>visualize_data
</h1>

<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Project high dim. data unto principal components (PCA) for visualization.</strong></div>

<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function visualize_data( X, k, IDX, types ) </strong></div>

<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Project high dim. data unto principal components (PCA) for visualization.

 Optionally IDX can be specified to indicate different classes for the points;
 in this case points in different classes are displayed using different colors.
 Up to 12 types are handled (for technical reasons involving plot), any 
 cluster with a label&gt;12 is assigned the label 12.

 INPUTS
   X       - column vector of data - N vectors of dimension p (X is Nxp)
   k       - dimension to which to reduce data (2 or 3)
   IDX     - [optional] cluster membership [see kmeans2.m]
   types   - [optional] cell array of length ntypes of text labels for each type 

 EXAMPLE
   X=[randn(100,5); randn(100,5)+4];
   IDX=[ones(100,1); 2*ones(100,1)];
   visualize_data( X, 2, IDX, {'type1','type2' });

 DATESTAMP
   29-Nov-2005  2:00pm

 See also <a href="kmeans2.html" class="code" title="function [IDX,C,sumd] = kmeans2( X,k,varargin )">KMEANS2</a></pre></div>

<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="pca.html" class="code" title="function [ U, mu, variances ] = pca( X )">pca</a>	principal components analysis (alternative to princomp).</li><li><a href="pca_apply.html" class="code" title="function [ Yk, Xhat, avsq, avsq_orig ] = pca_apply( X, U, mu, variances, k )">pca_apply</a>	Companion function to pca.</li><li><a href="../matlab/c.html" class="code" title="">c</a>	clc</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="democlassify.html" class="code" title="function democlassify">democlassify</a>	A demo used to test and demonstrate the usage of classifiers (clf_*)</li><li><a href="democluster.html" class="code" title="function [IDX, X, k_true, noisefrac_true, IDX_true ] =democluster( X, k, noisefrac, IDX_true )">democluster</a>	Clustering demo.</li><li><a href="nfoldxval.html" class="code" title="function CM=nfoldxval( data, IDX, clfinit, clfparams, types, ignoretypes, fname, show )">nfoldxval</a>	Runs n-fold cross validation on data with a given classifier.</li></ul>
<!-- crossreference -->



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